Abstract

Iris recognition is a biometric system for access control that uses the most unique characteristic of the human body, the iris employed in automated border crossings. This is Local binary pattern (LBP) is used for feature vectors extraction and Learning Vector Quantization (LVQ) performs classification. Here, matching is performed using the hamming distance of iris detections of matching. Iris images are selected from the CASIA Database, then the iris and pupil boundary are detected from rest of the eye image. It has been found that several accurate iris recognition algorithms use multiscale techniques, which provide a well-suited representation for iris recognition. Also we create Lab information of the users. All the images used in this paper were collected from the Chinese Academy Automation (CASIA) iris database VI.0 with 20 subjects in it.